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JINAV: Journal of Information and Visualization
Published by PT. Mattawang Mediatama Solution
ISSN : -     EISSN : 27461440     DOI : https://doi.org/10.35877/jinav
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science Mathematics
JINAV: Journal of Information and Visualization is an international peer-reviewed open-access journal dedicated to interchange for the results of high-quality research in all aspects of information science and technology, data, knowledge, communication, and their visualization. The journal publishes state-of-art papers in fundamental theory, experiments, and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion, and concise conclusion. As our commitment to the advancement of science and technology, the JINAV follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol. 7 No. 1 (2026)" : 5 Documents clear
Multi-Criteria Decision Support for Smart Manufacturing Innovation Ecosystems Toward Industry 6.0 Saputra, Dhanar Intan Surya; Sutiksno, Dian Utami; Rahim, Robbi
JINAV: Journal of Information and Visualization Vol. 7 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

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Abstract

The transition towards Industry 6.0 demands the evolution of intelligent manufacturing systems beyond automation and digitalization towards integrated, human-centric, and resilient innovation ecosystems. The assessment of ecosystem readiness in the face of such complexity is essentially a multi-criteria decision problem with conflicting objectives and structural risk constraints. This study proposes a non-compensatory multi-criteria decision support system using the ELECTRE method to evaluate the readiness of Smart Manufacturing Innovation Ecosystems in Indonesia. Seven industry 6.0-oriented criteria are considered, including technology infrastructure readiness, digital connectivity, human capital capability, sustainability, governance, cybersecurity, and investment costs. The structured decision matrix is normalized, weighted, and processed using concordance-discordance analysis to obtain an outranking dominance relationship between the decision alternatives. The results show that ecosystem readiness varies, with regions with balanced digital, governance, and cybersecurity readiness exhibiting structural dominance, while regions with lower digital readiness but lower investment costs are vetoed due to non-compensatory decision rules. Sensitivity analysis shows that the ranking of decision alternatives remains robust with moderate weight/threshold changes. The ELECTRE-based non-compensatory decision approach is more appropriate than compensatory approaches in evaluating strategic industrial constraints pertinent to the industry 6.0 transition. The study’s contribution is the operationalization of industry 6.0 principles within a decision support system framework, providing policy-relevant prioritization results pertinent to the smart manufacturing development strategy in Indonesia.
Option Pricing for Exchange Rate Hedging: Evaluation of Value at Risk, Sharpe Ratio, and Backtesting with Kupiec and Christoffersen Tests Muhtarulloh, Fahrudin; Humairo, Rd. Ilfah Syarifah; Meiza, Asti
JINAV: Journal of Information and Visualization Vol. 7 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

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Abstract

Exchange rate fluctuations are a major source of risk in international transactions. This study evaluates the effectiveness of currency options as hedging instruments using Value at Risk (VaR) and Sharpe Ratio, and assesses risk model accuracy through backtesting using the Kupiec and Christoffersen tests. Monthly closing prices of USD/IDR and MYR/IDR from January 2022 to January 2025 were obtained from Investing.com. Option pricing was calculated using the Garman–Kohlhagen model, while VaR estimation employed variance–covariance and historical methods. The results show that model accuracy varies across methods and currencies. The effectiveness of options as hedging instruments depends on model parameters, option type, and historical data characteristics.
Predicting Firm Value Using Ensemble and Nonlinear Machine Learning Models: Evidence from Financial and Value-Based Metrics Yanti Budiasih
JINAV: Journal of Information and Visualization Vol. 7 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

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Abstract

This study aims to predict firm value using financial indicators, economic profit, and intangible capital through machine learning approaches. The independent variables include precautionary cash, leverage, asset utilization, short-term liquidity ratio, economic profit, and intangible capital, while firm value is measured using Price-to-Book Value (PBV). This research employs several machine learning models, including Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Neural Network, and Support Vector Machine. Model performance is evaluated using Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The results show that the Random Forest model provides the best predictive performance, explaining approximately 90% of the variation in firm value. Asset utilization emerges as the most influential variable, followed by short-term liquidity ratio and economic profit. Meanwhile, leverage and precautionary cash show relatively smaller contributions to firm value prediction. These findings indicate that firm value is primarily influenced by operational efficiency, liquidity performance, and value creation capability. The study demonstrates that machine learning methods provide a comprehensive and effective approach to predicting firm value using financial and value-based performance indicators.
Rainfall Classification Using Output Statistics Models Based on Classification and Regression Trees with Principal Component Analysis Preprocessing Rais, Zulkifli; Hafid, Hardianti; Bunga, Yhegi Rombe
JINAV: Journal of Information and Visualization Vol. 7 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

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Abstract

Makassar City has a varied monsoon rainfall pattern, so rainfall prediction is an important challenge in disaster mitigation and resource management. Data mining techniques such as classification with the Classification and Regression Trees (CART) algorithm can be used to classify rainfall and analyze historical data, but the risk of overfitting high-dimensional data requires dimension reduction such as Principal Component Analysis (PCA). To improve accuracy, the Output Statistics Model (MOS) approach that combines numerical data and observations is also used. The results of dimension reduction using the Principal Component Analysis (PCA) method showed that of the initial seven variables, only three main components (, , and ) were retained because they had eigenvalues greater than 1 and were able to explain the data variance significantly. The decision tree model that was formed resulted in an accuracy rate of 72.34% in training data. Where the model can classify most of the training data into the correct rainfall category. In the data testing, the model was able to achieve an accuracy level of 71.43%, which shows that the model has good generalization ability to new data and does not experience overfitting.
Machine Learning Approaches for Predicting Regional Growth and Urban Expansion Tarigan, Ronald Rezeki
JINAV: Journal of Information and Visualization Vol. 7 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

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Abstract

Rapid urbanization requires accurate predictive models to support sustainable regional planning. This study proposes a Random Forest–based machine learning framework to predict regional growth and urban expansion using key indicators, including GDP growth, population density, infrastructure index, and night-time light intensity. The model demonstrates strong performance, achieving high accuracy (91%), R² of 0.94, and low RMSE (0.32), indicating robust predictive capability. Results show that infrastructure index, night-time light intensity, and population density are the most influential factors driving urban expansion, while GDP growth plays a secondary role. The model effectively captures non-linear relationships and produces predictions closely aligned with actual values across regions. The findings highlight the importance of spatial and infrastructural variables in shaping urban growth patterns. Methodologically, this study contributes a reproducible and interpretable framework, while practically offering insights for urban planning and policy formulation. The approach supports data-driven decision-making and promotes more efficient resource allocation. Future research should explore integration with remote sensing data, hybrid machine learning models, and spatio-temporal analysis to enhance predictive performance.

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